Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1706.00023 · Physical Review X

Low-Depth Quantum Simulation of Materials

Ryan Babbush, Nathan Wiebe, Jarrod McClean, James McClain, Hartmut Neven, Garnet Kin-Lic Chan

Published Mar 21, 2018• 334 citations
arXivHTMLPDF

Abstract

A proposed quantum algorithm for simulating the electronic structure of materials improves on the efficiency of current approaches, offering a path towards demonstrating quantum supremacy in physical applications.
DOI
10.1103/physrevx.8.011044

Related papers

Methods toward large-scale and highly-accurate quantum mechanics simulations of materials

Chen Huang

•Jan 1, 2011•0 citations

Quantum Simulation of Adaptive Topology Optimization Algorithm

Jincheng Zhang

Quantum simulation has emerged as a powerful tool for simulating complex systems, offering the potential for unprecedented accuracy and speed. This paper introduces a novel quantum simulation algorithm, termed "Adaptive Topology Optimization," designed to automatically adjust the simulation's topology during the process, thereby reducing simulation time and improving accuracy. The core mechanism revolves around leveraging the inherent dynamics of quantum systems to dynamically modify the simulation's structure, effectively optimizing the process for specific outcomes. This approach addresses a critical challenge in quantum simulation – the reliance on meticulously crafted parameters – and presents a promising path towards more efficient and precise simulations of intricate phenomena. The algorithm's potential applications span diverse fields, including materials science, drug discovery, and fundamental physics.

•Sep 2, 2026•0 citations

Towards near-term quantum simulation of materials

Laura Clinton, Toby S. Cubitt, Brian Flynn +6 more

Abstract Determining the ground and excited state properties of materials is considered one of the most promising applications of quantum computers. On near-term hardware, the limiting constraint on such simulations is the requisite circuit depths and qubit numbers, which currently lie well beyond near-term capabilities. Here we develop a quantum algorithm which reduces the estimated cost of material simulations. For example, we obtain a circuit depth improvement by up to 6 orders of magnitude for a Trotter layer of time-dynamics simulation in the transition-metal oxide SrVO3compared with the best previous quantum algorithms. We achieve this by introducing a collection of connected techniques, including highly localised and physically compact representations of materials Hamiltonians in the Wannier basis, a hybrid fermion-to-qubit mapping, and an efficient circuit compiler. Combined together, these methods leverage locality of materials Hamiltonians and result in a design that generates quantum circuits with depth independent of the system’s size. Although the requisite resources for the quantum simulation of materials are still beyond current hardware, our results show that realistic simulation of specific properties may be feasible without necessarily requiring fully scalable, fault-tolerant quantum computers, providing quantum algorithm design incorporates deeper understanding of the target materials and applications.

Nature Communications•Jan 24, 2024•59 citations

Quantum Matrix Algorithm for Electronic Structure Simulation A Novel Approach to Quantum Many-Body Problems Using Superposition and Entanglement

L. S. Mohamed

•Jan 1, 2026•1 citation

Quantum Simulation Self-Organized Structure Discovery

Jincheng Zhang

Quantum Simulation is rapidly transforming computational science, offering the potential to simulate complex quantum systems with unprecedented accuracy and efficiency. This paper introduces a novel quantum simulation algorithm, 'Quantum Weaver,' designed to autonomously discover and optimize self-organized structures within quantum systems. The algorithm leverages the power of quantum simulation to dynamically model system behavior and identify patterns indicative of self-organization, ultimately leading to significant improvements in computational efficiency. We present a detailed explanation of the algorithm's core mechanisms, highlighting the unique advantages it offers over existing approaches. This work represents a significant step toward automating the discovery of intricate quantum structures, offering a pathway toward accelerating research in areas such as materials science, drug discovery, and fundamental physics.

•Sep 3, 2026•0 citations

Quantum Computing for Electronic Structure and Sensor-Material Discovery

Mohsen Doust Mohammadi

•Aug 26, 2026•0 citations
Quantum simulation has emerged as a powerful tool for simulating complex systems, offering the potential for unprecedented accuracy and speed. This paper introduces a novel quantum simulation algorithm, termed "Adaptive Topology Optimization," designed to automatically adjust the simulation's topology during the process, thereby reducing simulation time and improving accuracy. The core mechanism revolves around leveraging the inherent dynamics of quantum systems to dynamically modify the simulation's structure, effectively optimizing the process for specific outcomes. This approach addresses a critical challenge in quantum simulation – the reliance on meticulously crafted parameters – and presents a promising path towards more efficient and precise simulations of intricate phenomena. The algorithm's potential applications span diverse fields, including materials science, drug discovery, and fundamental physics.
Abstract Determining the ground and excited state properties of materials is considered one of the most promising applications of quantum computers. On near-term hardware, the limiting constraint on such simulations is the requisite circuit depths and qubit numbers, which currently lie well beyond near-term capabilities. Here we develop a quantum algorithm which reduces the estimated cost of material simulations. For example, we obtain a circuit depth improvement by up to 6 orders of magnitude for a Trotter layer of time-dynamics simulation in the transition-metal oxide SrVO3compared with the best previous quantum algorithms. We achieve this by introducing a collection of connected techniques, including highly localised and physically compact representations of materials Hamiltonians in the Wannier basis, a hybrid fermion-to-qubit mapping, and an efficient circuit compiler. Combined together, these methods leverage locality of materials Hamiltonians and result in a design that generates quantum circuits with depth independent of the system’s size. Although the requisite resources for the quantum simulation of materials are still beyond current hardware, our results show that realistic simulation of specific properties may be feasible without necessarily requiring fully scalable, fault-tolerant quantum computers, providing quantum algorithm design incorporates deeper understanding of the target materials and applications.
Quantum Simulation is rapidly transforming computational science, offering the potential to simulate complex quantum systems with unprecedented accuracy and efficiency. This paper introduces a novel quantum simulation algorithm, 'Quantum Weaver,' designed to autonomously discover and optimize self-organized structures within quantum systems. The algorithm leverages the power of quantum simulation to dynamically model system behavior and identify patterns indicative of self-organization, ultimately leading to significant improvements in computational efficiency. We present a detailed explanation of the algorithm's core mechanisms, highlighting the unique advantages it offers over existing approaches. This work represents a significant step toward automating the discovery of intricate quantum structures, offering a pathway toward accelerating research in areas such as materials science, drug discovery, and fundamental physics.